LLM Agent Overview

When you encounter a problem that has no simple answer, you often need to follow several steps, think carefully, and remember what you have already tried. LLM agents are designed for exactly these situations in language model applications. They combine comprehensive data analysis, strategic planning, data retrieval, and the ability to learn from past actions to solve complex problems.

In this article, we will explore what LLM agents are, their benefits, capabilities, real-world examples, and the challenges they face.

What is LLM Agent?

LLM Agents are advanced AI systems designed to create complex texts that require sequential reasoning. They can think ahead, remember past conversations, and use different tools to adapt their responses to the desired situation and style.

Now, consider a detailed scenario:

What are the common legal challenges companies face under the new data privacy laws, and how are the courts addressing them?

This problem goes deeper than just finding facts. It’s about understanding the new rules, how they affect different companies, and seeing what the courts think about it all. A simple RAG system can extract relevant laws and cases, but it lacks the ability to relate those laws to actual business situations or deeply analyze court decisions.

In this case, LLM Agent comes into play when the project requires sequential reasoning, planning, and memory.

For this problem, the agent can decompose its task into multiple subtasks as shown below.

• • The first subtask might be to access a legal database to retrieve the latest laws and regulations.
• • Second, it establishes a historical baseline of previous treatments of similar issues.
• • Another subtask could be summarizing legal documents and predicting future trends based on observed patterns.

To complete these subtasks, the Agent needs a structured plan LLM, a reliable memory to track progress, and access to the necessary tools. These components form the backbone of the LLM Agent workflow.

LLM Agent Components

An LLM agent usually consists of four parts:

• • Agent/brain: Agent/brain
• • Planning:
• • Memory:
• • Tool use:

Agent/brain

The core of LLM Agent is the Large Language Model, which processes and understands language based on a large amount of trained data.

When you use the LLM Agent, you first give it a specific prompt. This prompt is crucial - it instructs the LLM how to respond, what tools to use, and what goals should be achieved during the interaction. It's like giving directions to a navigator before a journey.

Additionally, you can customize agents with specific roles. This means setting certain traits and expertise for an agent to make it better suited for a specific task or interaction. It's about tweaking the agent to perform tasks in a way that feels appropriate for the situation. Think history expert, legal expert, economics expert, and so on.

Essentially, at its core, LLM Agent combines advanced processing capabilities with customizable features to effectively handle and adapt to a wide variety of tasks and interactions.

Memory

Agents' LLM Memory helps them handle complex LLM tasks by recording previously completed work. There are two main types of memory:

• • **Short-term memory:** This is like the agent’s notepad where it can quickly write down important details during a conversation. It keeps track of the ongoing discussion and helps the model respond relevantly to the immediate environment. However, this memory is temporary and is cleared once the task at hand is completed.
• • **Long-term memory:** Think of it as an agent’s diary, storing insights and information from weeks or even months of past interactions. It’s not just about saving data; it’s about understanding patterns, learning from previous tasks, and calling on that information to make better decisions in future interactions.

By blending these two types of memory, the model can keep up with the current conversation and leverage the rich history of interactions. This means it can provide more targeted responses and remember the user's preferences over time, making each conversation feel more connected and relevant. Essentially, the agent is building an understanding that helps it serve you better with each interaction.

Planning

Through planning, LLM agents can reason, break down complex tasks into smaller, more manageable parts, and develop specific plans for each part. Agents can also reflect on and adjust their plans as the task evolves, ensuring they remain relevant to real-world situations. This adaptability is key to successful mission completion.

Planning typically consists of two main phases: plan formulation and plan reflection.

During the planning phase, the agent breaks down large tasks into smaller subtasks. Here are a few ways to do this:

• • Some task decomposition methods suggest creating a detailed plan once and then following it step by step.
• • The Chain of Thought (CoT) approach recommends a more adaptive strategy that allows the agent to handle subtasks one by one, thus providing greater flexibility.
• • The Train of Thought (ToT) tree is another method that takes the CoT technique further by exploring different ways to solve a problem. It breaks the problem into several steps, generates multiple ideas at each step, and arranges them like branches on a tree.

After developing a plan, it is important for the agent to review and evaluate its effectiveness. LLM-based agents use internal feedback mechanisms to leverage existing models to improve their strategies. They also interact with humans, adjusting their plans based on human feedback and preferences. Agents can also gather insights from their real and virtual environments, using outcomes and observations to further refine their plans.

Two effective approaches to incorporating feedback into planning are  ReAct ^[1]  and  Reflexion ^[2] .

Tools use

Tools here are various resources that help LLM Agent connect with the external environment to perform certain tasks. These tasks may include extracting information from databases, querying, coding, and anything else required for the agent to run. When the LLM agent uses these tools, it follows a specific workflow to perform tasks, collect observations, or collect information required to complete subtasks and satisfy user requests.

Here are some examples of how different systems integrate these tools:

• •  MRKL ^[3] (Modular Reasoning, Knowledge and Language): This system uses a set of expert modules, ranging from neural networks to simple tools like calculators or weather APIs. The main LLM acts as a router, directing queries to the appropriate expert module depending on the task.
• •  Toolformer ^[4]  and  TALM (Tool Augmented Language Model): ^[5] These models are specifically fine-tuned to interact effectively with external APIs. For example, the model can be trained to use financial APIs to analyze stock market trends or predict currency fluctuations, providing real-time financial insights directly to users.
• •  HuggingGPT: ^[6] This framework uses ChatGPT to manage tasks by selecting the best model available on the HuggingFace platform to handle a specific request and then summarizing the results.
• •  API-Bank ^[7] : A benchmark that tests the ability of LLMs to use 53 common APIs to handle tasks such as scheduling, health data management, or smart home control.

LLM Agent Framework

Let’s look at some notable LLM agents ^[8] and frameworks:

• •  Langchain ^[9]  - a framework for developing LLM-powered applications that simplifies the LLM application lifecycle.
• •  Llama Index ^[10] : A data framework that simplifies the creation of LLM applications through data connectors and structures, advanced search interactions, and integration capabilities.
• •  Haystack ^[11]  - an end-to-end NLP framework that enables you to build NLP applications.
• •  Embedchain ^[12]  - a framework for creating ChatGPT-like bots for your datasets.
• •  MindSearch ^[13] : A new AI search engine framework that works similarly to  Perplexity.ai Pro ^[14] . You can set it up as your own search engine using proprietary LLMs models like GPT and Claude or  open source models like InternLM2.5-7b-chat ^[15]  . It is designed to browse hundreds of web pages to answer any question, provide detailed answers and show how it found those answers.
• •  AgentQ ^[16] : helps create autonomous web agents that can plan, adapt, and self-correct. It  integrates guided Monte Carlo tree search (MCTS), AI self-criticism, and  RLHF ^[18] using the Direct Preference Optimization (DPO) ^[17] algorithm .
• •  Nvidia NIM Agent Blueprint ^[19] : An agent for enterprise developers who need to build and deploy custom GenAI applications.
• •  Bee Agent Framework ^[20] : An open source framework from IBM for building, deploying, and servicing large agent workflows at scale. IBM’s goal for Bee is to enable developers to adopt the latest open source and proprietary models while making minimal changes to current agent implementations.

LLM Agent Challenge

While LLM agents are extremely useful, they do present several challenges that we need to consider:

• • Limited context: LLM agents can only keep track of a limited amount of information at a time. This means they may not remember important details from earlier in a conversation or miss key instructions. While technologies such as vector stores help by providing access to more information, they cannot fully solve this problem.
• • Difficulty in long-term planning: LLM agents have difficulty planning over the long term. They often have trouble adapting when unexpected problems arise, which can make them less flexible than how humans solve problems.
• •  Inconsistent output : Because LLM agents rely on natural language to interact with other tools and databases, they can sometimes produce unreliable output. They may make formatting errors or fail to follow instructions correctly, which can lead to errors in the tasks they perform.
• • **Adapt to specific roles:** LLM agents need to be able to handle different roles depending on the task at hand. However, fine-tuning them to understand and perform uncommon roles or align with different human values ​​is a complex challenge. The latest MOE models may be better able to solve this problem.
• • **Prompt dependency:** The LLM agent operates based on prompt words, but these prompts need to be very precise. Even small changes can lead to big errors, so creating and optimizing these prompts can be a delicate process. This is where a small mistake can lead to a big error.
• • **Manage knowledge:** Keeping LLM Agents’ knowledge accurate and unbiased is tricky. They must have the right information to make smart decisions, but too much irrelevant information can cause them to draw wrong conclusions or act based on outdated facts.